The antenna weight optimization plays a vital role in improving the key performance indicators (KPIs) of massive multi-input multi-output (MIMO) systems. Due to complicated channel characteristics and numerous parameter combinations, conventional methods suffer from suboptimal performance and unaffordable complexity. Additionally, the implementation of existing intelligent algorithms in the practical system is limited by heavy overhead and potential instability associated with environmental interactions. In this paper, we propose a digital twin assisted optimization framework comprising a conditional generative adversarial network (C-GAN) aided digital twin and a deep reinforcement learning (DRL) based massive MIMO antenna weight optimization algorithm to maximize the KPI of coverage. The C-GAN aided digital twin is built to augment system performance data and offer high-precision KPI pre-validation by fitting the mapping between beamforming schemes and system performance along with the distribution of system performance over user positions. The DRL based optimization algorithm that achieves a better complexity-performance tradeoff is combined with digital twin for reduced training overhead and safe exploration. Simulation results based on quasi deterministic radio channel generator (QuaDRiGa) verify that compared to intelligent optimization methods directly conducted in practical systems, our proposed framework can reduce the overhead while achieving comparable performance by leveraging high-precision pre-validation capabilities of the proposed digital twin.

In millimeter wave (mmWave) systems with the typical two-stage hybrid precoding structure, it is necessary to determine the optimal transmit (TX)-and-receive (RX) beam pairs through beam training. However, existing beam training methods are time-consuming and costly, making them unsuitable for cell-free mmWave systems with a large number of TX-RX pairs. In this paper, we propose a fast and efficient beam training method that adopts a hierarchical codebook design and requires only a small number of wide-sweeping beams to achieve the optimal beam pairs based on deep learning and compressed sensing. Additionally, it simultaneously supports the user-centric cell-free access point (AP) clustering and the corresponding AP beam training process. The experimental results demonstrate that our approach can accurately predict the effective beams with high accuracy while significantly reducing the beam training time and overhead.

Speculative execution attacks such as Spectre and Meltdown exploit the wrong execution patch to leak private data. In current state-of-the-art defense strategies, executions of all memory accesses that use speculatively-loaded addresses are blocked, resulting in high overhead. Our key observation is that these blocked memory accesses can be executed without operand-dependent hardware resource usage through value prediction. Therefore, we propose a novel hardware defense framework, named Speculative Value Prediction (SVP), to safely and efficiently execute the potentially unsafe memory accesses earlier. We build SVP on the cycle-accurate Gem5 simulator and its performance improvement is positively correlated with the coverage of value predictors. Experiments show that when using the value predictor with 30%/60%/100% coverage, SVP outperforms the state-of-the-art defense mechanism STT in the Spectre model by 21.5%/50.3%/107.7% respectively, and in the Futuristic model by 28.7%/55.4%/105.7% respectively.

With the widespread use of massive multi-input-multi-output (MIMO) technology in current wireless networks, network optimization faces much higher costs due to the significantly increased angular space. Digital twin (DT), as a promising tool to enhance the effectiveness and efficiency of performance evaluation, still faces many challenges for massive MIMO optimization, where the complex channel characteristics and the system performance uncertainty over randomly distributed user equipment (UE) position both make it difficult to obtain an explicit relationship expression between the beamforming parameters at the base station (BS) and the system performance. In this article, we propose a conditional generative adversarial network (C-GAN) based digital twin network (DTN), which can fit the mapping from the beamforming to the system performance and match the distribution of system performance under a certain beamforming configuration over different UE position simultaneously. Moreover, it provides a generalized way for pre-validation of different key performance indicators (KPIs) and further raises the accuracy via data augmentation. QuaDRiGa based simulations validate the effectiveness of our proposed method in system performance modeling and KPI prediction.

In urban driving scenarios, it is a key component for autonomous vehicles to generate a smooth, kinodynamically feasible, and collision-free path. We present an optimization-based path planning method for autonomous vehicles navigating in cluttered environment, e.g., roads partially blocked by static or moving obstacles. Our method first computes a collision-free reference line using quadratic programming(QP), and then using the reference line as initial guess to generate a smooth and feasible path by iterative optimization using sequential quadratic programming(SQP). It works within a fractions of a second, thus permitting efficient regeneration.

Fifth-generation (5G) wireless communications confront explosive growth in mobile data demand and massive intensive user equipment (UE) connections. The optimization of base station (BS) array orientations in large-scale networks can provide high potential performance gain to meet these requirements. However, traditional schemes are highly dependent on experience and difficult in implementation due to their demand on repeated drive tests and large amounts of data samples. In this paper, via exploiting UE locations and channel direction information, we propose an intelligent network optimization framework to maximize the long-term network rate performance. For the augmentation of limited drive test data, a deep Gaussian process regression (DGPR) model is designed to construct a scenario-specific channel modeling. In addition, via a domain-knowledge driven fusion of convolutional neural network (CNN) and multi-layer perceptron (MLP), we propose a multi-branch deep neural network (DNN) to accurately map BS array orientations and concise channel measurements to the network performance. Finally, based on the scenario-specific modeling, an efficient gradient search approach is proposed to optimize BS array orientations via neural network (NN) backpropagation. Both simulations and field tests in 5G experimental networks validate the effectiveness and efficiency of our proposed framework, especially with limited drive test data.